One step further, by taking advantage of the AEVB algorithm, recent studies have introduced deep generative models for anomaly detection. The work [1]} uses Variational Auto-Encoder (VAE) [2]} for anomaly detection on non-sequential datasets. However, vanilla VAE makes i.i.d assumption among data points \(\mathbf {x}=\lbrace \mathbf {x}^{(i)} \rbrace _{i=1}^{N}\) , which is unrealistic for real-world time series data. To solve this problem, the very recent approach [3]} further extends vanilla VAE by preprocessing the raw data with sliding windows, and the windows are assumed to obey i.i.d. Although this approach mitigates the artifact, while the temporal structure is still not well modeled. Alternatively, researchers seek to use a deterministic Recurrent Neural Network (RNN) [4]} to produce hidden states, then the latent variable is conditioned on the RNN hidden states and the previous latent variables, such that the temporal structure is captured. The resulting models include VRNN [5]}, STORN [6]}, which can be viewed as sequential versions of VAE. However, these models essentially hardly consider the presence of anomalies in their objective functions.
